Gram Schmidt with R

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青春惊慌失措
青春惊慌失措 2020-12-31 21:30

Here is a MATLAB code for performing Gram Schmidt in page 1 http://web.mit.edu/18.06/www/Essays/gramschmidtmat.pdf

I am trying for hours and hours to perform this wi

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  •  天涯浪人
    2020-12-31 22:04

    Just for fun I added an Armadillo version of this code and benchmark it

    Armadillo code :

    #include 
    // [[Rcpp::depends(RcppArmadillo)]]
    
    using namespace Rcpp;
    
    //[[Rcpp::export]]
    List grahm_schimdtCpp(arma::mat A) {
        int n = A.n_cols;
        int m = A.n_rows;
        arma::mat Q(m, n);
        Q.fill(0);
        arma::mat R(n, n);
        R.fill(0);  
        for (int j = 0; j < n; j++) {
        arma::vec v = A.col(j);
        if (j > 0) {
            for(int i = 0; i < j; i++) {
            R(i, j) = arma::as_scalar(Q.col(i).t() *  A.col(j));
            v = v - R(i, j) * Q.col(i);
            }
        }
        R(j, j) = arma::norm(v, 2);
        Q.col(j) = v / R(j, j);
        }
        return List::create(_["Q"] = Q,
                         _["R"] = R
        );
        }
    

    R code not optimized (directly based on algorithm)

    grahm_schimdtR <- function(A) {
        m <- nrow(A)
        n <- ncol(A)
        Q <- matrix(0, nrow = m, ncol = n)
        R <- matrix(0, nrow = n, ncol = n)
        for (j in 1:n) {
        v <- A[ , j, drop = FALSE]
            if (j > 1) {
        for(i in 1:(j-1)) {
                R[i, j] <- t(Q[,i,drop = FALSE]) %*% A[ , j, drop = FALSE]
                v <- v - R[i, j] * Q[ ,i]
        }
        }
        R[j, j] = norm(v, type = "2")
        Q[ ,j] = v / R[j, j]
        }
    
        list("Q" = Q, "R" = R)
    
    }
    

    Native QR decomposition in R

    qrNative <- function(A) {
        qrdec <- qr(A)
        list(Q = qr.R(qrdec), R = qr.Q(qrdec))
    }
    

    We will test it with the same matrix as in original document (link in the post above)

    A <- matrix(c(4, 3, -2, 1), ncol = 2)
    
    all.equal(grahm_schimdtR(A)$Q %*% grahm_schimdtR(A)$R, A)
    ## [1] TRUE
    
    all.equal(grahm_schimdtCpp(A)$Q %*% grahm_schimdtCpp(A)$R, A)
    ## [1] TRUE
    
    all.equal(qrNative(A)$Q %*% qrNative(A)$R, A)
    ## [1] TRUE
    

    Now let's benchmark it

    require(rbenchmark)
    set.seed(123)
    A <- matrix(rnorm(10000), 100, 100)
    benchmark(qrNative(A),
              grahm_schimdtR(A),
              grahm_schimdtCpp(A),
              order = "elapsed")
    ##                  test replications elapsed relative user.self
    ## 3 grahm_schimdtCpp(A)          100   0.272    1.000     0.272
    ## 1         qrNative(A)          100   1.013    3.724     1.144
    ## 2   grahm_schimdtR(A)          100  84.279  309.849    95.042
    ##   sys.self user.child sys.child
    ## 3    0.000          0         0
    ## 1    0.872          0         0
    ## 2   72.577          0         0
    

    I really love how easy to port code into Rcpp....

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